Unacceptable Experiences Whilst Attending University and Their Association With Student Mental Health, Well-Being and Academic Outcomes
Bibliographic record
Abstract
Introduction: Unacceptable experiences (UEs) are commonly reported by undergraduate students. However, their prevalence and impact have not been rigorously studied. Objectives: To (1) estimate the prevalence of UEs, (2) identify high-risk subgroups, and (3) examine associations with mental health and academic outcomes among undergraduate students attending Queen's University. Methods: Data from the 2021/2022-2023/2024 cycles of the U-Flourish Student Well-Being Survey were collected at the beginning and completion of each academic year. Validated symptom measures included the GAD-7 (anxiety), PHQ-9 (depression), C-SSRS (suicidal thoughts and behaviours), and WEMWBS-7 (well-being). Self-reported UEs included: sexual violence or harassment, physical assault, bullying, hate crimes, and discrimination. Multivariable regression examined associations between UEs and student mental health, and grade-point average (GPA) from linked university data. Results: One-third (28.9%) of students (n=797/2,757) reported an unacceptable experience over the academic year. Sexual violence or harassment and discrimination (15%) were most frequently reported, followed by bullying/harassment (11%), hate crimes (4.6%) and physical assault (3.3%). UEs were highest in students who identified as non-binary gender (49%), 2SLGBTQIA+ sexuality (39%) or had a history of mental illness (41%). These UEs increased the risk of reporting clinically significant anxiety and depressive symptoms by 5-18% and 11-40%, respectively, and Students reporting UEs were also more likely (2-59%) to report having suicidal thoughts and behaviours over the academic year, particularly those who experienced sexual violence (RR:1.59; 95% CI:1.10-2.23). There was evidence that sexual violence, bullying, and hate crimes were associated with lower GPAs among first-year students. Conclusion: UEs are common among undergraduate students and are associated with significant negative mental health and well-being outcomes. Further research is needed to better understand the nature of these UEs and the mechanisms by which they impact student mental health and academic performance. This information is pivotal to develop evidence-informed prevention and early intervention initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".